Bilateral filtering abundance features for multilayer unmixing

Multilayer learning has shown promising performance in machine learning due to its strong capacity in data representation. The core idea is associated with the generation and the use of high-level features for task learning. Motivated by visual and brain learning mechanism, differential abundance features are proposed in this study as high-level features for further unmixing to refine obtained unmixing results. Kernel archetypal analysis (KAA) shows potential value for spectral unmixing which realizes endmember extraction and abundances estimation simultaneously. In this paper, the abundances after bilateral filtering which incorporates influence of both spatial and spectral information of neighbor pixels are used for further unmixing by cascade KAA to improve abundance estimation. The developed method was tested on both synthetic and real hyperspectral image data which demonstrate that multilayer unmxing with the filtering abundance features outperforms the conventional unmixing approach.